Scalytics

Your data team spent 6 months building a pipeline. The business question it answers changed after 3 month. This is why 87% of enterprise AI projects never make it to production. Not because the models are wrong—because the data infrastructure can't keep up with the questions. We built something different. → ai-ready.scalytics.io No sales pitch. Just a diagnostic to see if your stack can actually support the AI use cases your executives are asking for. Takes 3 minutes. Lasts forever.

How storage-native streaming separates batch and AI workloads from Kafka brokers. Open source KafScale architecture with direct S3 access and Iceberg integration.

Most Kafka deployments today carry a cost structure they no longer need. Kafka solved real problems. It standardized the streaming protocol. It decoupled producers and consumers. The problem is not Kafka. The problem is that most Kafka clusters still assume storage and compute must live together. That assumption made sense when object storage was slow. It no longer does. Broker disks now determine: => how long you can retain data => how painful recovery is => how expensive analytics become => how often scaling causes operational work This is why Kafka operations get harder at scale. Streaming and analytics compete for the same brokers. Retention becomes a capacity problem instead of a policy decision. Recovery means moving data instead of restarting services. We already saw this pattern once. HDFS looked inevitable until S3 changed the economics. Hadoop did not disappear. Its architecture became optional. The same transition is happening in streaming. Storage-native streaming keeps the Kafka protocol but moves data out of the broker entirely. Events live in object storage. Brokers become stateless. Scaling stops moving data. Batch analytics stop touching the streaming path. This is not experimental. Multiple vendors and open source projects are already building this way. If you run Kafka at scale, you will encounter this architecture whether you plan for it or not. Read the story: 👉 https://lnkd.in/eaZ5r99F

Explore how big data architectures evolved from Apache Hadoop to Apache Spark to Apache Wayang. Research-backed analysis of cross-platform data processing.

Explore how big data architectures evolved from Apache Hadoop to Apache Spark to Apache Wayang. Research-backed analysis of cross-platform data processing.

Data environments are no longer homogeneous. Modern analytics routinely spans databases, stream platforms, distributed processing engines, and object storage. Traditional single-platform assumptions no longer hold as organizations scale and diversify their data infrastructure. In our latest article, we explore how big data processing has evolved from Apache Hadoop’s batch-oriented execution to Apache Spark’s in-memory model, and now to Apache Wayang’s cross-platform optimization. The article draws on peer-reviewed research, benchmark results, and architectural analysis to explain why cross-platform data processing matters for enterprises running analytics across heterogeneous systems. Read the full article to understand the evolution, the limitations of single-platform approaches, and how cross-platform execution can reduce integration overhead and operational complexity: https://lnkd.in/edH2ETc4 #BigData #DataArchitecture #DistributedSystems #ApacheWayang

We are pleased to share a new interview with Alexander Alten, co-founder and CTO, where he outlines the principles that guide our work at Scalytics and explains why federated data processing will become essential for modern Data Analytics and AI systems. The conversation highlights the practical challenges enterprises face when data cannot be centralized and why bringing computation to the data is now a requirement for scalable AI. It also reflects how the experience of our founding team and our work on Apache Wayang shaped the design of our platform and its focus on privacy, reliability and operational simplicity. The full interview is available here: https://lnkd.in/gEBgpDCs We welcome your thoughts and questions.

Apache Wayang in Action: Enabling Data Systems Integration via a Unified Data Analytics Framework Apache Wayang is an open-source framework, which provides a systematic and efficient solution for unifying data analytics over disparate data sources and via integrating multiple heterogeneous data systems. It achieves that by decoupling applications from the underlying systems. In addition, it provides an optimizer so that users do not have to specify the platforms on which their pipeline should run but the optimizer can determine the best way given a cost metric. In this demonstration, we showcase how the flexible architecture of Wayang enables seamless integration with multiple heterogeneous data systems and how the query optimizer can lead to better performance. https://lnkd.in/ePD_G2E6

Are you?

Are you truly AI-ready for 2026? Many companies plan AI initiatives — but most fail because their data isn’t ready. The Scalytics AI Readiness Assessment shows you in 3 minutes whether your organization is prepared: Architecture, data quality, governance, security, real-time capability, private AI. Scalytics unifies your data sources into an AI-ready foundation — for regulated industries and growth-driven companies alike. Start 2026 with data that can actually deliver AI value. → ai-ready.scalytics.io